# Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

arXiv: https://arxiv.org/abs/2106.13876 · HTML: https://mlsanity.com/paper/2106.13876/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 73 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 30 of 59)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems](https://mlsanity.com/paper/1705.04146/) (2017)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Rationalizing Neural Predictions](https://mlsanity.com/paper/1606.04155/) (2016)
- [Visual Entailment: A Novel Task for Fine-Grained Image Understanding](https://mlsanity.com/paper/1901.06706/) (2019)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)
- [Multimodal Explanations: Justifying Decisions and Pointing to the Evidence](https://mlsanity.com/paper/1802.08129/) (2018)

## Built on by (most cited first, 24 of 24)

- [Faithfulness Tests for Natural Language Explanations](https://mlsanity.com/paper/2305.18029/) (2023)
- [How Interpretable are Reasoning Explanations from Prompting Large Language Models?](https://mlsanity.com/paper/2402.11863/) (2024)
- [Explaining Chest X-ray Pathologies in Natural Language](https://mlsanity.com/paper/2207.04343/) (2022)
- [FaithLM: Towards Faithful Explanations for Large Language Models](https://mlsanity.com/paper/2402.04678/) (2024)
- [Learning by Self-Explaining](https://mlsanity.com/paper/2309.08395/) (2023)
- [Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup](https://mlsanity.com/paper/2112.06204/) (2021)
- [Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods](https://mlsanity.com/paper/2210.07222/) (2022)
- [Learning to Follow Object-Centric Image Editing Instructions Faithfully](https://mlsanity.com/paper/2310.19145/) (2023)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?](https://mlsanity.com/paper/2302.10766/) (2023)
- [DeViL: Decoding Vision features into Language](https://mlsanity.com/paper/2309.01617/) (2023)
- [Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks](https://mlsanity.com/paper/2311.05085/) (2023)
- [Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios](https://mlsanity.com/paper/2406.00343/) (2024)
- [Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations](https://mlsanity.com/paper/2212.04231/) (2022)
- [To Tell The Truth: Language of Deception and Language Models](https://mlsanity.com/paper/2311.07092/) (2023)
- [ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer](https://mlsanity.com/paper/2309.08583/) (2023)
- [Explaining Agent Behavior with Large Language Models](https://mlsanity.com/paper/2309.10346/) (2023)
- [Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation](https://mlsanity.com/paper/2311.18062/) (2023)
- [Graph-Guided Textual Explanation Generation Framework](https://mlsanity.com/paper/2412.12318/) (2024)
- [SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations](https://mlsanity.com/paper/2305.13235/) (2023)
- [Using Natural Language Explanations to Improve Robustness of In-context Learning](https://mlsanity.com/paper/2311.07556/) (2023)
- [Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correction](https://mlsanity.com/paper/2502.15261/) (2025)
- [Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs](https://mlsanity.com/paper/2602.21763/) (2026)
- [WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification](https://mlsanity.com/paper/2603.18752/) (2026)

## Abstract (arXiv)

Models that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI. While an extractive rationale provides a quick view of the features most responsible for a prediction, an NLE allows for a comprehensive description of the decision-making process behind a prediction. However, current models that generate the best extractive rationales or NLEs often fall behind the state-of-the-art (SOTA) in terms of task performance. In this work, we bridge this gap by introducing RExC, a self-rationalizing framework that grounds its predictions and two complementary types of explanations (NLEs and extractive rationales) in background knowledge. Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations. Furthermore, a perturbation analysis in RExC shows a high degree of association between explanations and predictions, a necessary property of faithful explanations.